The machine learning feature in X-Pack reacts quickly to anomalous input, learning new
behaviors in data. Highly anomalous input increases the variance in the models
whilst the system learns whether this is a new step-change in behavior or a
one-off event. In the case where this anomalous input is known to be a one-off,
then it might be appropriate to reset the model state to a time before this
event. For example, you might consider reverting to a saved snapshot after Black
Friday or a critical system failure.

(boolean) If true, deletes the results in the time period between the
latest results and the time of the reverted snapshot. It also resets the
model to accept records for this time period. The default value is false.

If you choose not to delete intervening results when reverting a snapshot,
the job will not accept input data that is older than the current time.
If you want to resend data, then delete the intervening results.